We detail here a new model to handle nested entity recognition. The model is an auto-regressive encoder-only Transformer [1]} taking as input a sequence of words and a list of entity mentions already extracted (empty list at the first iteration) and predicts as output a list of new mentions.
The entities predicted at each iteration do not overlap, but all the entities predicted at the end of the iterations may overlap.
